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Package ‘CRImage’March 25, 2020
Type Package
Title CRImage a package to classify cells and calculate tumourcellularity
Version 1.34.0
Date 2012-10-01
AuthorHenrik Failmezger <[email protected]>, Yinyin Yuan <[email protected]>, Os-car Rueda <[email protected]>, Flo-rian Markowetz <[email protected]>
Maintainer Henrik Failmezger <[email protected]>, Yinyin Yuan
Description CRImage provides functionality to process and analyze images, in particular to clas-sify cells in biological images. Furthermore, in the context of tumor images, it provides function-ality to calculate tumour cellularity.
License Artistic-2.0
LazyLoad yes
Imports MASS, e1071, foreach, sgeostat
Depends EBImage, DNAcopy, aCGH
Collate plotCorrectedCN.R correctCopyNumber.R writeDensityImage.RconvertRGBToHSV.R convertHSVToRGB.R imageCompression.RcreateBinaryImage.R colorCorrection.R searchStructures.RsegmentStructures.R classifyStructures.R numberOfNeighbors.RsegmentCytoplasma.R segmentImage.R createClassifier.RkernelSmoother.R paintCells.R classifyCells.RdetermineCellularity.R calculateCellularity.R findSlices.RparseFinalScan.R classificationAperio.R processAperio.RPhansalkar_threshold.R SauvolaThreshold.RcalculateMeanStdTarget.R convertLABToRGB.R convertRGBToLAB.RlocalORThreshold.R oregonThreshold.R localThreshold.RcalculateOtsu.R classifyPen.R getImageDistance.R hist3d.RlabelCells.R plotImage.R
biocViews CellBiology, Classification
git_url https://git.bioconductor.org/packages/CRImage
git_branch RELEASE_3_10
git_last_commit a318c37
1
2 CRImage-package
git_last_commit_date 2019-10-29
Date/Publication 2020-03-24
R topics documented:CRImage-package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2calculateCellularity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3calculateMeanStdTarget . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5calculateOtsu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6classifyCells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7colorCorrection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8convertHSVToRGB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9convertLABToRGB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10convertRGBToHSV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11convertRGBToLAB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11correctCopyNumber . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12createBinaryImage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13createClassifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15labelCells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16plotCorrectedCN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17processAperio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18SauvolaThreshold . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20segmentImage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
Index 23
CRImage-package CRImage is a package to analyze images and classify cells.
Description
CRImage allows classification of cells in biological images. It offers methods to segment cellsor cell nuclei in biological images for example HE stained images. It offers methods to create aclassifier and to classify cells in these images. Furthermore it allows the calculation of tumourcellularity for large microscope images.
CRImage makes use of the image processing package EBImage, which uses the ’ImageMagick’library for image I/O operations and the ’GTK’ library to display images.
Details
Package: CRImageType: PackageVersion: 1.0Date: 2010-04-27License: LGPL Version 2 or laterLazyLoad: yes
calculateCellularity 3
Package content
Image processing methods:
• calculateThreshold
• segmentImage
Classification:
• createTrainingSet
• createClassifier
• classifyCells
Tumour cellularity
• calculateCellularity
• processAperio
Author(s)
Henrik Failmezger, <[email protected]>
Yinyin Yuan, <[email protected]>
Oscar Rueda, <[email protected]>
Florian Markowetz, <[email protected]>
CRI Cambridge
Li Ka Shing Centre
Robinson Way
Cambridge, CB2 0RE, UK
Ludwigs-Maximilians University of Munich
Examples
example(segmentImage)example(createClassifier)example(classifyImage)
calculateCellularity Calculation of tumour cellularity
Description
The function calculates the tumour cellularity of an image by counting tumour and non tumourcells.
Usage
calculateCellularity(filename="",image=NA,classifier=NULL,cancerIdentifier=NA,KS=FALSE,maxShape=NA,minShape=NA,failureRegion=NA,colors=c(),threshold="otsu",classesToExclude=c(),numWindows=2,classifyStructures=FALSE,pixelClassifier=NA,ksToExclude=c(),densityToExclude=c(),numDensityWindows=4)
4 calculateCellularity
Arguments
filename A path to an image file.
image If filename is undefined, an Image object
classifier A SVM object, created with createClassifier or directly with the package e1071cancerIdentifier
A string which describes, how the cancer class is named.
KS Apply kernel smoother?
maxShape Maximum size of cell nuclei
minShape Minimum size of cell nuclei
failureRegion minimum size of failure regions
colors Colors to paint the classes
threshold Which threshold should be uses, "otsu" or "phansalkar"classesToExclude
Should a class be excluded from cellularity calculation?
numWindows Number of windows for the threshold.classifyStructures
Use hierarchical classification. If yes a pixel classifier has to be defined.pixelClassifier
A SVM to classify pixel based on their color values. Needed if hierarchicalclassification should be applied.
ksToExclude These classes are excluded from kernel smoothing.densityToExclude
This class is excluded from cellularity calculation.numDensityWindows
Number of windows for the density plot.
Details
The method calculates tumour cellularity of an image. The cells of the image are classified and thecellularity is: numTumourCells/numPixel. Furthermore the number of cells of the different classesare counted. A heatmap of cellularity is created. The image is divided in 16 subwindows andcellularity is calculated for every subwindow. Green in the heatmaps indicates strong cellularity,white low cellularity.
Value
A list containing
cellularity values
a vector, the n first values indicate the n numbers of cells in the n classes, then + 1th value indicates the tumour cellularity, The n + 2th value is the ratio oftumour cells by all cells
cancerHeatmap Heatmap of cancer density
Author(s)
Henrik Failmezger, [email protected]
calculateMeanStdTarget 5
Examples
t = system.file("extdata", "trainingData.txt", package="CRImage")#read training datatrainingData=read.table(t,header=TRUE)#create classifierclassifier=createClassifier(trainingData)[[1]]#calculation of cellularityf = system.file("extdata", "exImg.jpg", package="CRImage")exImg=readImage(f)cellularity=calculateCellularity(classifier=classifier,filename=f,KS=TRUE,maxShape=800,minShape=40,failureRegion=2000,classifyStructures=FALSE,cancerIdentifier="c",numDensityWindows=2,colors=c("green","red"))
calculateMeanStdTarget
Calculates Mean and Standard deviation of an image
Description
Mean and SD calculation
Usage
calculateMeanStdTarget(imgT)
Arguments
imgT the Image to calculate.
Details
Mean and SD
Value
Vector with mean and standard deviation.
Author(s)
Henrik Failmezger, [email protected]
Examples
#read the target imagef1= system.file("extdata", "exImg2.jpg", package="CRImage")targetImage=readImage(f1)#read the image whose color values should be adaptedf2= system.file("extdata", "exImg3.jpg", package="CRImage")imgToConvert=readImage(f2)#calculate mean and standard deviation of target color channelsmst=calculateMeanStdTarget(targetImage)# create a white pixel mask
6 calculateOtsu
whitePixelMask=imgToConvert[,,1]>0.85 & imgToConvert[,,2]>0.85 & imgToConvert[,,3]>0.85#adapt color channels of imageimgCorrected=colorCorrection(imgToConvert,mst,whitePixelMask)
calculateOtsu Does Otsu thresholding
Description
The function applies Otsu thresholding on the image.
Usage
calculateOtsu(allGreyValues)
Arguments
allGreyValues Vector of grey values.
Details
The function calculates a value which separates the grey value histogram the best in foreground andbackground.
Value
the threshold
Author(s)
Henrik Failmezger, [email protected]
References
Nobuyuki Otsu: A threshold selection method from grey level histograms. In: IEEE Transactionson Systems, Man, and Cybernetics. New York 9.1979, S.62-66. ISSN 1083-4419
See Also
calculateThreshold localOtsuThreshold
Examples
f1= system.file("extdata", "exImg2.jpg", package="CRImage")print(f1)img=readImage(f1)print(img)#convert to grayscaleimgG=EBImage::channel(img,'grey')#threshold valuet=calculateOtsu(as.vector(imgG))
classifyCells 7
classifyCells A function to classify cells
Description
The function classifies cells and paints the different class types in the image.
Usage
classifyCells(classifier,filename="",image=NA,segmentedImage=NA,featuresObjects=NA,paint=TRUE,KS=FALSE,cancerIdentifier=NA, maxShape=NA,minShape=NA,failureRegion=NA,colors=c(),classesToExclude=c(),threshold="otsu",numWindows=2,structures=NA,classifyStructures=FALSE,pixelClassifier=NA,ksToExclude=c())
Arguments
classifier A Support Vector Machine created by createClassifier or directly by the packagee1071
filename A path to an image file.image An ’Image’ object or an array.segmentedImage An ’Image’ object or an array.The corresponding segmented image (created by
segmentImage)featuresObjects
Cell feature file of the segmentedImage (created by segmentImage)paint If true, the classified cells are painted with different colors in the imageKS Use Kernel Smoohter in classification?cancerIdentifier
A string which describes, how the cancer class is named.maxShape Maximum size of cell nucleiminShape Minimum size of cell nucleifailureRegion minimum size of failure regionscolors Colors to paint the classesclassesToExclude
Which class should be excluded?threshold Which thresholding method should be used, "otsu" or "phansalkar"numWindows Number of windows to use for thresholding.structures If the image is already segmented, structures can be inserted to enable hierarchi-
cal classification.classifyStructures
Use hierarchical classification. If yes a pixel classifier has to be defined.pixelClassifier
A SVM to classify pixel based on their color values. Needed if hierarchicalclassification should be applied.
ksToExclude These classes are excluded from kernel smoothing.
Details
The kernels smoother improves the classification for cells which are likely to occur in clusters, liketumour cells. The kernel smoothing method can only be applied for two classes. If there are moreclasses only the normal svm without kernel smoothing is applied. Different classes are labeled withdifferent colors in the image.
8 colorCorrection
Value
A list with
comp1 classes
comp2 Classes, painted in the image, if paint was true
Author(s)
Henrik Failmezger, [email protected]
Examples
t = system.file("extdata", "trainingData.txt", package="CRImage")#read training datatrainingData=read.table(t,header=TRUE)#create classifierclassifier=createClassifier(trainingData)[[1]]#classify cellsf = system.file("extdata", "exImg.jpg", package="CRImage")classesValues=classifyCells(classifier,filename=f,KS=TRUE,maxShape=800,minShape=40,failureRegion=2000)
colorCorrection Color transfer between images.
Description
The colors of one image are adapted to the colors of a target image.
Usage
colorCorrection(imgO, meanStdTarget,whiteMask = c())
Arguments
imgO The image who’s colors should be adapted
meanStdTarget Array with mean and standard deviation of the target image.
whiteMask Boolean mask of white pixel in the image. These pixels are excluded from colorcorrection.
Details
Mean and standard deviation of the target image can be calculated using the function calculate-MeanStdTarget.
Value
The image with adapted colors.
Author(s)
Henrik Failmezger, [email protected]
convertHSVToRGB 9
References
Reinhard, E.; Adhikhmin, M.; Gooch, B.; Shirley, P.; , "Color transfer between images," ComputerGraphics and Applications, IEEE , vol.21, no.5, pp.34-41, Sep/Oct 2001 doi: 10.1109/38.946629URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=946629&isnumber=20481
See Also
calculateMeanStandardTarget
Examples
#read the target imagef1= system.file("extdata", "exImg2.jpg", package="CRImage")targetImage=readImage(f1)#read the image whose color values should be adaptedf2= system.file("extdata", "exImg3.jpg", package="CRImage")imgToConvert=readImage(f2)#calculate mean and standard deviation of target color channelsmst=calculateMeanStdTarget(targetImage)# create a white pixel maskwhitePixelMask=imgToConvert[,,1]>0.85 & imgToConvert[,,2]>0.85 & imgToConvert[,,3]>0.85#adapt color channels of imageimgCorrected=colorCorrection(imgToConvert,mst,whitePixelMask)
convertHSVToRGB Conversion from HSV color space to RGB color space
Description
The function converts images in the HSV colour space to the RGB colour space.
Usage
convertHSVToRGB(imgHSV)
Arguments
imgHSV An ’Image’ object or an array in the HSV colour space.
Details
Standard colour space conversion.
Value
An array in the RGB colour space.
Author(s)
Henrik Failmezger, [email protected]
10 convertLABToRGB
See Also
convertRGBToHSV convertRGBToLAB convertLABToRGB
Examples
f= system.file("extdata", "exImg.jpg", package="CRImage")img=readImage(f)#conversion to RGB color spaceimgRGB=convertHSVToRGB(img)
convertLABToRGB Conversion of LAB colour space to RGB colour space
Description
Color space conversion.
Usage
convertLABToRGB(imgLAB)
Arguments
imgLAB LAB channel vectors.
Details
Color space conversion
Value
RGB channel vectors.
Author(s)
Henrik Failmezger, [email protected]
Examples
f= system.file("extdata", "exImg.jpg", package="CRImage")img=readImage(f)#conversion to HSV color spaceimgRGB=convertLABToRGB(img)
convertRGBToHSV 11
convertRGBToHSV Conversion from RGB color space to HSV color space
Description
The RGB Image is converted to an HSV image.
Usage
convertRGBToHSV(img)
Arguments
img The RGB image
Details
The entries of the array are Hue, Saturation and Value.
Value
The image in HSV color space.
Author(s)
Henrik Failmezger, [email protected]
See Also
convertHSVToRGB convertRGBToLAB convertLABToRGB
Examples
f= system.file("extdata", "exImg.jpg", package="CRImage")img=readImage(f)#conversion to HSV color spaceimgHSV=convertRGBToHSV(img)
convertRGBToLAB Converts RGB to LAB color space.
Description
Conversion of Color spaces.
Usage
convertRGBToLAB(imgT)
Arguments
imgT The RGB image.
12 correctCopyNumber
Details
Color space conversion
Value
The image in LAB color space.
Author(s)
Henrik Failmezger, [email protected]
Examples
f= system.file("extdata", "exImg.jpg", package="CRImage")img=readImage(f)#conversion to LAB color spaceimgLAB=convertRGBToLAB(img)
correctCopyNumber Allelic Copy Number correction for cellularity
Description
This function segments copy number and corrects log-ratios (LRR) and beta allele frequencies(BAF) values for cellularity.
Usage
correctCopyNumber(arr="Sample1", chr=NULL, p=NULL, z=NULL, min.value=-5)
Arguments
arr Name of the array.
chr Chromosome to run. If NULL, all chromosomes are run.
p Percentage of tumoural cells.
z Copy Number Data. Must be a dataframe with the following columns: Name(id of the probe), Chr (chromosome), Pos (position), LRR (log ratios) and BAF(beta allele frequencies).
min.value Value assigned to the probes that have 0 copies after correction.
Details
The data.frame z must contain only SNP probes, that is probes with both LRR and BAF values. It isrecommended that all replicated probes are merged so the positions are unique. This function callsDNAcopy to segment the LRR and then correct the segmented profiles for normal contaminationaccording to the method described in the reference below (see for details).
createBinaryImage 13
Value
A list with 2 components:
y a data.frame with as many rows as probes containing the following variables:Chrom (chromosome), Pos (position), Orig.LRR (LRR before correction) Orig.BAF(BAF before correction), Corr.LRR (LRR after cellularity correction) and Corr.BAF(BAF after correction)
seg a data.frame with the segmented data. Contains the following columns: ID(name of the array), chrom (chromosome), loc.start (start of the region), loc.end(end of the region), num.mark (number of probes in the region), seg.mean (LRRof the region), BAF (BAF of the regions), num.BAF (number of SNP probes inthe region), Sa (estimated absolute copy number for the first allele), Sb (esti-mated absolute copy number for the first allele), LRR.tum (corrected LRR forthe region), BAF.tum (corrected BAF for the region).
Note
Includes an adaptation of aCGH mergeLevels function to fix a problem with ansari.test.
Author(s)
Oscar M. Rueda, [email protected]
References
Yuan, Y et al. Quantitative image analysis of cellular heterogeneity in primary breast tumors en-riches genomic assays. In prep.
Examples
LRR <- c(rnorm(100, 0, 1), rnorm(10, -2, 1), rnorm(20, 3, 1),rnorm(100,0, 1))
BAF <- c(rnorm(100, 0.5, 0.1), rnorm(5, 0.2, 0.01), rnorm(5, 0.8, 0.01), rnorm(10, 0.25, 0.1), rnorm(10, 0.75, 0.1),rnorm(100,0.5, 0.1))
Pos <- sample(x=1:500, size=230, replace=TRUE)Pos <- cumsum(Pos)Chrom <- rep(1, length(LRR))z <- data.frame(Name=1:length(LRR), Chrom=Chrom, Pos=Pos, LRR=LRR, BAF=BAF)res <- correctCopyNumber(arr="Sample1", chr=1, p=0.75, z=z)
createBinaryImage Thresholding
Description
Creates a binary image from a grayscale image by thresholding.
Usage
createBinaryImage(imgG,img=NULL,method="otsu",threshold=NULL,numWindows=1,whitePixelMask=c())
14 createBinaryImage
Arguments
img An Image object or an array.
imgG The grey valued Image object.
method Either "otsu" or "phansalkar"
threshold Fixed threshold
numWindows Number of windows to use for threshold calculation.
whitePixelMask Boolean mask of white pixels, if they should be excluded from thresholding
Details
The functions returns the binary image resulting from the thresholding. If threshold is defined, allpixels smaller than this value will be fixed to 1 all other values will be set to 0. If threshold is unde-fined, the thresholding value is calculated automatically using ’otsu’ or ’phansalkar’ thresholding.
The function ’otsu’ does Otsu thresholding on the grey level histograms of the image. The function’phansalkar’ does thresholding using the mean and standard deviation of a specified window. Thethresholding is done on the RGB as well as the LAP color space and the results are ORed. Thewindow size is dim(img)/numWindows. White pixel can be excluded from thresholding (e.g. ifwhite is background) by defining a whitePixelMask
Value
The binary image.
Author(s)
Henrik Failmezger, [email protected]
References
Neerad Phansalkar, Sumit More, Ashish Sabale, Dr. Madhuri Joshi, "Adaptive Local Thresholdingfor Detection of Nuclei in Diversly Stained Cytology Images," 2011 IEEE International Conferencein Communications and Signal Processing (ICCSP), pp. 218, 10 Feb. 2011
Nobuyuki Otsu: A threshold selection method from grey level histograms. In: IEEE Transactionson Systems, Man, and Cybernetics. New York 9.1979, S.62-66. ISSN 1083-4419
Examples
f= system.file("extdata", "exImg.jpg", package="CRImage")img=readImage(f)#conversion to grayscaleimgG=EBImage::channel(img,"gray")imgB=createBinaryImage(imgG,img=img,method="otsu",numWindows=4)#white pixel maskwhitePixelMask=img[,,1]>0.85 & img[,,2]>0.85 & img[,,3]>0.85#exclude white pixels from thresholdingimgB=createBinaryImage(imgG,img=img,method="otsu",numWindows=4,whitePixelMask)#phansalkar thresholdimgB=createBinaryImage(imgG,img=img,method="phansalkar",numWindows=4)
createClassifier 15
createClassifier Construction of a classifier
Description
Creates a classifier for a training set.
Usage
createClassifier(trainingData, cross = FALSE)
Arguments
trainingData A table, created by segmentImage with manually added classes.
cross Does 10-fold cross validation to test the classifiers performance.
Details
Topological features include the density of cells and the size of the surrounding cytoplasma of acell. These features depend on the size of the image. If training image and the image to classifyhave different size, these features can fool the classification and should not be enabled.
Value
A List containing:
classifier The classifier
performance cross validation performance
Author(s)
Henrik Failmezger, [email protected]
See Also
’createTrainingSet’,’classifyCells’
Examples
f = system.file("extdata", "trainingData.txt", package="CRImage")#read training datatrainingData=read.table(f,header=TRUE)#create classifierclassifier=createClassifier(trainingData)[[1]]
16 labelCells
labelCells Interactive Session for cell labeling
Description
The functions creates an interactive session in order to label cells with their classes. The labeledcells can be used as training set for the classifier. Note!! This is until now only tested for MacOsX.
Usage
labelCells(img, segmentedImage, classes, classColours, nblocks = 3, labeledPoints = NULL, filename = NULL, filenameImage = NULL,transformCoordinates=FALSE)
Arguments
img The image.
segmentedImage The segmented image.
classes The possible class labels.
classColours The colors for the class labels.
nblocks The image can be separated in several blocks, as zooming is not possible.
labeledPoints Labeled cells from a previous training session.
filename The table of labeled cells is saved at this location.
filenameImage The image with the labeled cells is saved at this location.transformCoordinates
deprecated
Details
Use the keys: a: In order to add a label to a cell. d: In order to delete a label from a cell. c: Toswitch between classes. q: To quit the interactive session. r: To refresh the session (labeled cellswill be shown after refreshing)
Value
A table with columns: index: the index of the cell in the segmented image. x: x-coordinate ofthe cell y: y-coordinate of the cell classCell: Label of the cell xLocal: Local x coordinate in thesubimage(block) yLocal: Local y coordinate in the subimage(block) block: Block number in whichthe cell arises.
Author(s)
Henrik Failmezger
Examples
##---- Should be DIRECTLY executable !! ----##-- ==> Define data, use random,##--or do help(data=index) for the standard data sets.
## The function is currently defined as
plotCorrectedCN 17
plotCorrectedCN Plot CN profiles corrected for cellularity
Description
This function takes the result of a call to correctCopyNumber and plots the results.
Usage
plotCorrectedCN(CN, chr=NULL)
Arguments
CN object result of a call to correctCopyNumber.
chr chromosome to plot.
Details
A panel with four plots is created. The top panel shows LRR (with DNAcopy segmentation over-layed) and BAF before correction and the bottom panel shows the plots after correction.
Value
No value is returned.
Author(s)
Oscar M. Rueda, [email protected]
References
Yuan, Y et al. Quantitative image analysis of cellular heterogeneity in primary breast tumors en-riches genomic assays. In prep.
Examples
LRR <- c(rnorm(100, 0, 1), rnorm(10, -2, 1), rnorm(20, 3, 1),rnorm(100,0, 1))
BAF <- c(rnorm(100, 0.5, 0.1), rnorm(5, 0.2, 0.01), rnorm(5, 0.8, 0.01), rnorm(10, 0.25, 0.1), rnorm(10, 0.75, 0.1),rnorm(100,0.5, 0.1))
Pos <- sample(x=1:500, size=230, replace=TRUE)Pos <- cumsum(Pos)Chrom <- rep(1, length(LRR))z <- data.frame(Name=1:length(LRR), Chrom=Chrom, Pos=Pos, LRR=LRR, BAF=BAF)res <- correctCopyNumber(arr="Sample1", chr=1, p=0.75, z=z)plotCorrectedCN(res, chr=1)
18 processAperio
processAperio Cellularity Calculation of Aperio TX Scanner
Description
Procession of Aperio TX Slides.
Usage
processAperio(classifier=classifier,inputFolder=inputFolder,outputFolder=outputFolder,identifier=identifier,numSlides=numSlides,cancerIdentifier=cancerIdentifier,classOther = NA,maxShape=800,minShape=40,failureRegion=2000,slideToProcess=NA,KS=TRUE,colors=c(),classesToExclude=c(),threshold="otsu",numWindows=2,colorCorrection=FALSE,classifyStructures=FALSE,ksToExclude=c(),pixelClassifier=NA,densityToExclude=c(),numDensityWindows=32,resizeFactor=4,plotCellTypeDensity=TRUE,greyscaleImage=0,penClassifier=NULL,referenceHist=NULL,fontSize = 10)
Arguments
classifier The classifier.
inputFolder The path to the image folder.
outputFolder The path to the output folder.
identifier The identifier of the files ("Ss" or "Da")
numSlides The number of sections in the image.cancerIdentifier
The identifier of the cancer class
classOther deprecated
maxShape Maximum size of cell nuclei
minShape Minimum size of cell nuclei
failureRegion minimum size of failure regions
slideToProcess Set this parameter if only a certain slide should be processed
KS Apply Kernel Smoother?
colors Colors to paint the classesclassesToExclude
Which class should be excluded?
threshold Which thresholding method should be used, "otsu" or "phansalkar" possible
numWindows Number of windows to use for thresholding.colorCorrection
deprecatedclassifyStructures
Use hierarchical classification. If yes a pixel classifier has to be defined.
ksToExclude These classes are excluded from kernel smoothing.pixelClassifier
A SVM to classify pixel based on their color values. Needed if hierarchicalclassification should be applied.
densityToExclude
This class is excluded from cellularity calculation.numDensityWindows
Number of windows for the density plot.
processAperio 19
resizeFactor Specifies the size of the cell density image. If this variable is not defined, thesize of the thumbnail is used for the cell density image, else the size is calculatedby size(thumbnail)*resizeFactor. The thumbnail is the small overview image,created by the Aperio software.
plotCellTypeDensity
Plot the density of different cell types?
greyscaleImage Color channel of the RGB image that should be used for thresholding
penClassifier Classifier to exclude low quality images (will be part of next release)
referenceHist Colour Histogram of a reference image that can be used to calculate the qualityof the recent image. (will be part of next release)
fontSize will be part of next release
Details
The function processes images of Aperio TX scanners. The images have to be saved in the CWSformat.
Value
Four folders are created in the output folder.
Files Cellularity values and cell numbers are saved in the fileclassifiedImage
Subimages with labeled tumour and non tumour cells
tumourDensity Cancer heatmaps for every subimagecellCoordinates
Coordinates and cell class for every cell in the subimage
resizeFactor Size of the cellularity density image, calculated by size(thumbnail) * resizeFac-tor. Whereas the thumbnail is the small overview image produced by Aperio.
Author(s)
Henrik Failmezger, [email protected]
Examples
#t = system.file("extdata", "trainingData.txt", package="CRImage")#read training data#trainingData=read.table(t,header=TRUE)#create classifier#classifier=createClassifier(trainingData,topo=FALSE)[[1]]#classify aperio#f = system.file("extdata", package="CRImage")#f=file.path(f,"8905")#dir.create("AperiOutput")#takes long time!
f = system.file("extdata", package="CRImage")fc=file.path(f,"testClassifier")load(fc)fp=file.path(f,"pixelClassifier")load(fp)pixelClassifier=model
20 SauvolaThreshold
pathToImage=file.path(f,"8905")
pathToOutput="" #specify an output folder here
#processAperio(classifier=classifier,inputFolder=pathToImage,outputFolder=pathToOutput,identifier="Da",numSlides=1,cancerIdentifier="c",maxShape=800,minShape=40,failureRegion=2000,slideToProcess=1,KS=FALSE,colors=c("white","green","blue","brown"),classesToExclude=c('a','l','nl'),threshold="otsu",ksToExclude=c('l','nl'),pixelClassifier=pixelClassifier,classifyStructures=TRUE,densityToExclude=c('a'),numDensityWindows=8,resizeFactor=1.5,plotCellTypeDensity=FALSE,greyscaleImage=1)
SauvolaThreshold Do Sauvola thresholding
Description
Thresholding method using mean and standard deviation.
Usage
SauvolaThreshold(allGreyValues)
Arguments
allGreyValues Vector of gray values.
Details
A threshold for the gray values is returned
Value
The threshold.
Author(s)
Henrik Failmezger, [email protected]
References
J. Sauvola, M. Pietikainen, "Adaptive Document Image Binarization," Pattern Recognition, vol. 33,225-236, 2000
See Also
createBinaryImage
Examples
f1= system.file("extdata", "exImg2.jpg", package="CRImage")print(f1)img=readImage(f1)print(img)#convert to grayscaleimgG=EBImage::channel(img,'grey')#threshold valuet=SauvolaThreshold(as.vector(imgG))
segmentImage 21
segmentImage Segmentation of an image
Description
The function segments cells or cell nuclei in the image.
Usage
segmentImage(filename="",image=NA,maxShape=NA,minShape=NA,failureRegion=NA,threshold="otsu",numWindows=2, colorCorrection=FALSE, classifyStructures=FALSE,pixelClassifier=NULL,greyscaleImage=0,penClassifier=NULL,referenceHist=NULL)
Arguments
filename A path to an image
image An ’image’ object, if no filename is specified.
maxShape Maximum size of cell nuclei
minShape Minimum size of cell nuclei
failureRegion minimum size of failure regions
threshold Thresholding method, "otsu" or "phansalkar"
numWindows Number of windows to use for thresholding.colorCorrection
deprecatedclassifyStructures
Segment structures in the image, if yes a pixel classifier has to be definedpixelClassifier
A SVM which classifies RGB color values in foreground and background.
greyscaleImage Channel of the RGB image, to use for thresholding, if 0 use a joined greyscaleimage.
penClassifier Classifier to exclude low quality images(will be part of next release)
referenceHist Color histogram of a reference image, that can be used to estimate the quality ofthe recent image (will be part of next release)
Details
The image is converted to greyscale and thresholded. Clutter is deleted using morphological oper-ations. Clustered objects are separated using watershed algorithm. Segmented Cell nuclei, whichexceed the maximum size are thresholded and segmented again. Cell nuclei which fall below theminimum size are deleted. Dark regions which exceed the parameter failureRegion are consideredas artefacts and deleted. If the parameters are not defined, the operations will not be executed.Features are generated for every segmented object.
Value
A list is returned containing
image The original imagesegmented image
The segmented image
22 segmentImage
Author(s)
Henrik Failmezger, [email protected]
References
EBImage, ’http://www.bioconductor.org/packages/release/bioc/html/EBImage.html’
Examples
#segment image#f = system.file('extdata' ,'exImg.jpg',package='CRImage')#segmentationValues=segmentImage(f,maxShape=800,minShape=40,failureRegion=2000,threshold="otsu",numWindows=4)#image=segmentationValues[[1]]#segmentedImage=segmentationValues[[2]]#imageFeatures=segmentationValues[[3]]
Index
∗Topic misccalculateCellularity, 3calculateMeanStdTarget, 5calculateOtsu, 6classifyCells, 7colorCorrection, 8convertHSVToRGB, 9convertLABToRGB, 10convertRGBToLAB, 11correctCopyNumber, 12createBinaryImage, 13createClassifier, 15CRImage-package, 2labelCells, 16plotCorrectedCN, 17processAperio, 18SauvolaThreshold, 20segmentImage, 21
calculateCellularity, 3calculateMeanStdTarget, 5calculateOtsu, 6classifyCells, 7colorCorrection, 8convertHSVToRGB, 9convertLABToRGB, 10convertRGBToHSV, 11convertRGBToLAB, 11correctCopyNumber, 12createBinaryImage, 13createClassifier, 15CRImage (CRImage-package), 2CRImage-package, 2
labelCells, 16
plotCorrectedCN, 17processAperio, 18
SauvolaThreshold, 20segmentImage, 21
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